Some times, you want to plot the 3d-surfaces from freesurfer. Here, it is easier to use data from freesurfer (like annot and colorlut files) to create colours for the vectors. The data is somewhat more complex than the 2d ggplot polygon version ggseg ggseg3d() will create a plotly plot, which is interactive, and provides another type of flexibility to the user. A lot of credit goes to A.M.Winkler and his Brainder work, which supplied us with the first examples of going from .srf to .ply files, and whose scripts massively aided us in making this work.
The function ggseg3d(), is based in the plotly, it is recommended to get a little familiarized with with plotly.
Out-of-the-box, ggseg() works without supplying any extra information. It will create a base plot of the aparc (dk) brain segmentations. All [...]_3d atlases have a built in colour column for default colour plotting of the segments.
The data is stored in tibbles, and looks like so:
## # A tibble: 6 x 4
## atlas surf hemi ggseg_3d
## <chr> <chr> <chr> <list>
## 1 dk_3d inflated left <tibble [36 × 8]>
## 2 dk_3d inflated right <tibble [36 × 8]>
## 3 dk_3d LCBC left <tibble [36 × 8]>
## 4 dk_3d LCBC right <tibble [36 × 8]>
## 5 dk_3d white left <tibble [36 × 8]>
## 6 dk_3d white right <tibble [36 × 8]>To grab all the data for a surface and hemisphere, you should reduce the data to one line, and then unnest()
## # A tibble: 36 x 11
## atlas surf hemi region colour mesh label roi annot acronym lobe
## <chr> <chr> <chr> <chr> <chr> <list> <chr> <chr> <chr> <chr> <chr>
## 1 dk_3d infla… right <NA> <NA> <name… rh_med… 0001 media… mdlw <NA>
## 2 dk_3d infla… right banks s… #196428 <name… rh_ban… 0002 banks… bnst temp…
## 3 dk_3d infla… right caudal … #7D64A0 <name… rh_cau… 0003 cauda… cdac fron…
## 4 dk_3d infla… right caudal … #641900 <name… rh_cau… 0004 cauda… cdmf fron…
## 5 dk_3d infla… right corpus … <NA> <name… rh_cor… 0005 corpu… cc <NA>
## 6 dk_3d infla… right cuneus #DC1464 <name… rh_cun… 0006 cuneus cuns occi…
## 7 dk_3d infla… right entorhi… #DC140A <name… rh_ent… 0007 entor… entr temp…
## 8 dk_3d infla… right fusiform #B4DC8C <name… rh_fus… 0008 fusif… fsfr temp…
## 9 dk_3d infla… right inferio… #DC3CDC <name… rh_inf… 0009 infer… infp pari…
## 10 dk_3d infla… right inferio… #B42878 <name… rh_inf… 0010 infer… inft temp…
## # … with 26 more rowsParticularly notice the mesh column, which is a list column of lists. In there is all the 6 vectors needed to create the mesh of the tri-surface plot. You’ll also need to notive the label, annot and region columns, which are likely the columns you will be matching on when proviging with your own data for colours. You need to be meticulous when fixing your data, be sure it matches. The function should give you a warning if it’s struggling to match something.
The column you want to use for colour, needs to be supplied to the colour option, and you’ll likely want to supply it to the text option, as this will add another line to the plotly hover information.
You can provide custom colour palettes either in hex or R-names
ggseg3d(.data = someData, atlas = dk_3d,
colour = "p", text = "p",
palette = c("forestgreen", "white", "firebrick"))A new improvement now allows you to also supply a named vector as palette, to control the breakpoints of the palette values, and allow you to have a colour bar that exceeds the values that exist in the data plotted.
ggseg3d(.data = someData, atlas = dk_3d,
colour = "p", text = "p",
palette = c("forestgreen" = 0, "white" = .05, "firebrick" = 1))If you are plotting the sub-cortical structures, you might want to reduce the opacity of the NA structures, so that you can see the more medial structures. you may also want to add the glassbrain.